IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v389y2010i8p1601-1606.html
   My bibliography  Save this article

A note on the sum of the sample autocorrelation function

Author

Listed:
  • Hassani, Hossein

Abstract

It is shown that the sum of the sample autocorrelation function at lag h≥1 is always −12 for any stationary time series with arbitrary length T≥2 (Hassani, 2009 [1]). In this paper, the distribution of a set of the sample autocorrelation function using the properties of this quantity is considered. It is found that the distribution of a set of the sample autocorrelation estimates is not independent and identically distributed. This finding implies that the result of diagnostic check and model building using the traditional assumption of iid can be quite misleading.

Suggested Citation

  • Hassani, Hossein, 2010. "A note on the sum of the sample autocorrelation function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1601-1606.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:8:p:1601-1606
    DOI: 10.1016/j.physa.2009.12.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437109010644
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2009.12.050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    2. Hassani, Hossein & Yeganegi, Mohammad Reza, 2020. "Selecting optimal lag order in Ljung–Box test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Hassani, Hossein & Yeganegi, Mohammad Reza, 2019. "Sum of squared ACF and the Ljung–Box statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 81-86.
    4. Hassani, Hossein & Leonenko, Nikolai & Patterson, Kerry, 2012. "The sample autocorrelation function and the detection of long-memory processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6367-6379.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Razmi, Fatemeh & Azali, M. & Chin, Lee & Shah Habibullah, Muzafar, 2016. "The role of monetary transmission channels in transmitting oil price shocks to prices in ASEAN-4 countries during pre- and post-global financial crisis," Energy, Elsevier, vol. 101(C), pages 581-591.
    2. Arouna, Aminou & Fatognon, Irene Akoko & Saito, Kazuki & Futakuchi, Koichi, 2021. "Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development," World Development Perspectives, Elsevier, vol. 21(C).
    3. McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020. "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, vol. 87(C), pages 383-393.
    4. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    5. Carlos Alberto Orge Pinheiro & Valter de Senna, 2016. "Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BMeFbovespa," Brazilian Business Review, Fucape Business School, vol. 13(5), pages 129-157, September.
    6. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
    7. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
    8. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    9. Andrea Saayman & Jacques de Klerk, 2019. "Forecasting tourist arrivals using multivariate singular spectrum analysis," Tourism Economics, , vol. 25(3), pages 330-354, May.
    10. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    11. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    12. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    13. Fatemeh Razmi & Azali Mohamed & Lee Chin & Muzafar Shah Habibullah, 2015. "The Role of Monetary Policy in Macroeconomic Volatility of Association of Southeast Asian Nations-4 Countries against Oil Price Shock over Time," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 731-737.
    14. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    15. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    16. Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
    17. Moody Chu & Matthew Lin & Liqi Wang, 2014. "A study of singular spectrum analysis with global optimization techniques," Journal of Global Optimization, Springer, vol. 60(3), pages 551-574, November.
    18. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.
    19. Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
    20. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:389:y:2010:i:8:p:1601-1606. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.